In this work, a Bayesian semiparametric multivariate model is fitted to study data related to in-hospital and 60-day survival probabilities of patients admitted to a hospital with ST-elevation myocardial infarction diagnosis. We consider a hierarchical generalized linear model to predict survival probabilities and a process indicator (time of intervention). Poisson-Dirichlet process priors, generalizing the well-known Dirichlet process, are considered for modeling the random-effect distribution of the grouping factor which is the hospital of admission.

A Semiparametric Bayesian Multivariate Model for Survival Probabilities After Acute Myocardial Infarction / E. Prandoni, A. Guglielmi, F. Ieva, A. Paganoni - In: The Contribution of Young Researchers to Bayesian Statistics / [a cura di] F. Ieva, E. Lanzarone. - Springer Proceedings in Mathematics & Statistics. - [s.l] : Springer International Publishing Switzerland 2014, 2014 Jan. - ISBN 978-3-319-02083-9. - pp. 161-163 (( Intervento presentato al 1. convegno BAYSM 2013 - first BAyesian Young Statistician Meeting tenutosi a CNR IMATI (Milan, Italy) nel 2013 [10.1007/978-3-319-02084-6__31].

A Semiparametric Bayesian Multivariate Model for Survival Probabilities After Acute Myocardial Infarction

F. Ieva
Penultimo
;
2014

Abstract

In this work, a Bayesian semiparametric multivariate model is fitted to study data related to in-hospital and 60-day survival probabilities of patients admitted to a hospital with ST-elevation myocardial infarction diagnosis. We consider a hierarchical generalized linear model to predict survival probabilities and a process indicator (time of intervention). Poisson-Dirichlet process priors, generalizing the well-known Dirichlet process, are considered for modeling the random-effect distribution of the grouping factor which is the hospital of admission.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/247970
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